GLMix Model Segmentation for Applicant Qualification Prediction
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Solution Overview
Problem
Existing machine learning models for predicting applicant qualification are inefficient due to their large size, slow adaptation, inability to learn detailed patterns, and data leakage issues during retraining, leading to unreliable predictions and quality testing.
Innovation Solution
A fully automated process for frequent retraining and redeployment of a generalized mixed effects (GLMix) model, which includes a global model and personalized random effects models, using a holdout dataset to prevent data leakage and allowing for rapid adaptation by applying preliminary labels for non-reaction scenarios, ensuring deterministic data splitting, and leveraging hyper-personalized components for improved predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single global model is trained on all user and job posting data patterns, then the model size is large and reliable over large candidate pools, but the model is too large and slow to train frequently and too limited in capacity to learn detailed member or job posting-specific patterns
Solution Approach 1:
The patent segments the single global model into multiple specialized models: a global model trained on all data, per-user models trained on individual user data, and per-job posting models trained on individual job posting data. This segmentation allows each model to be smaller and train faster while collectively maintaining comprehensive coverage and reliability.
Solution Approach 2:
The patent adds a new dimension to the modeling approach by introducing user-specific and job posting-specific models alongside the global model. This multi-dimensional model hierarchy enables simultaneous learning of general patterns and specific patterns without requiring a single massive model.
2Adaptability or versatility
If a single global model is trained on all user and job posting data patterns, then the model can learn general patterns, but the model fails to take into account cases where the particular user is significantly more or less qualified for a job posting than their type would suggest
Solution Approach 1:
The patent applies local quality by training separate per-user models and per-job posting models that capture specific characteristics of individual users and job postings. These localized models provide precise predictions for specific user-job posting pairs, supplementing the general patterns learned by the global model.
Solution Approach 2:
The patent creates a dynamic model selection system that adapts which model to use based on the specific prediction context. The system can dynamically switch between global model predictions and user-specific or job posting-specific model predictions to optimize accuracy for each case.
3Reliability
If labels are assigned based on whether the applicant was successful in applying for the job posting, then the training data reflects actual outcomes, but the data becomes stale because it takes months to determine the outcome
Solution Approach 1:
The patent applies preliminary action by assigning provisional labels to training data based on available intermediate information (such as application status, interview scheduling, or recruiter responses) before the final hiring outcome is determined. This allows the model to be trained on fresher data that reflects the current state of the application process.
Solution Approach 2:
The patent implements periodic updates to training labels as new information becomes available during the hiring process. Rather than waiting for the final outcome, the system periodically re-evaluates and updates labels based on intermediate milestones, keeping the training data current throughout the hiring timeline.
4Ease of operation
If test data is randomly selected from a data set for model quality testing, then the testing process is simple, but a data leakage problem occurs where the data used to test a model may have been used to train the model
Solution Approach 1:
The patent segments the data set into distinct training data and test data subsets using deterministic splitting based on user identifiers and job posting identifiers. This ensures that data used for training one model does not leak into the test set for evaluating that model, while maintaining a simple and systematic splitting process.
Solution Approach 2:
The patent introduces an intermediary mechanism (deterministic hash-based splitting) that separates training and test data based on entity identifiers. This intermediary approach prevents direct random selection from causing data leakage while maintaining operational simplicity through automated, reproducible data partitioning.
Data Source
AI summary
In an example embodiment, a fully automated process is provided for frequent model retraining and redeployment of a machine learned model trained to output a prediction of how likely it is that a candidate is qualified for a particular job posting. Model quality verification is provided by maintaining a snapshot of a baseline model and automatically comparing it to a proposed model by performing various metrics on the models by testing the models using a holdout data set that includes only data that was not used during the training process. Overlap between data in the holdout set used during retraining and the training set used during initial training is prevented by splitting each dataset using a hash on certain fields of the data.